Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/vinnie357/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/vinnie357/claude-skills/research-skill)<a href="https://agentmods.dev/commands/vinnie357/claude-skills/research-skill"><img src="https://agentmods.dev/badge/commands/vinnie357/claude-skills/research-skill/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/vinnie357/claude-skills/research-skill"><img src="https://agentmods.dev/badge/commands/vinnie357/claude-skills/research-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00011 | $0.00754 |
| Opus 5 | $0.00005 | $0.00377 |
| Sonnet 5 | $0.00002 | $0.00151 |
| Haiku 4.5 | $0.00001 | $0.00075 |
Grade A, and why
research-skill scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Research a topic and create a properly structured Agent Skill following the Agent Skills Specification.
Skill Creation:
- Directory Structure: Creates
skills/<skill-name>/SKILL.mdwith proper frontmatter - Progressive Disclosure: Generates reference files in
references/for deep context - Source Documentation: Updates
promptlog/sources.mdwith all research sources - Specification Compliance: Follows Agent Skills Specification v1.0
Features:
- Automatic Complexity Assessment: Evaluates topic complexity (1-10 scale)
- Thinking Mode Selection: Standard/Extended/Deep based on complexity
- Manual Override: Use
--complexity=<level>to force thinking depth - YAML Frontmatter: Auto-generates name, description, license, metadata
- Reference Management: Creates separate files for detailed documentation
- Source Tracking: Maintains traceability in promptlog/sources.md
Examples:
/research-skill elixir-genserver
# Creates: skills/elixir-genserver/SKILL.md
# Updates: promptlog/sources.md
/research-skill kubernetes-operators --complexity=high
# Creates: skills/kubernetes-operators/SKILL.md
# skills/kubernetes-operators/references/
# Updates: promptlog/sources.md
/research-skill react-hooks --complexity=medium
# Creates: skills/react-hooks/SKILL.md with enhanced analysis
SKILL.md Structure:
---
name: skill-name
description: What the skill does and when to use it — this is the ONLY text
Claude sees during discovery, so every activation trigger belongs here, not
in a body section.
license: MIT
---
# Skill Name
## Core Concepts
[Essential knowledge]
## Best Practices
[Guidelines and patterns]
## Examples
[Concrete usage examples]
## References
[Links to reference files if needed]
Workflow:
- Research: Gather authoritative sources and best practices
- Structure: Create skill directory following spec
- Generate SKILL.md: Write frontmatter and core content
- Create References: Add detailed docs in references/ for progressive disclosure
- Document Sources: Update promptlog/sources.md with all sources used
- Validate: Ensure spec compliance and activation clarity
Task Instructions: Use Agent tool with subagent_type: "general-purpose" to:
- Research the topic thoroughly using web search and authoritative sources
- Create the skill directory:
skills/<skill-name>/ - Generate SKILL.md with:
- Proper YAML frontmatter (name, description, license)
- Clear activation criteria in description
- Core procedural knowledge in markdown body
- Concrete examples and patterns
- Create
skills/<skill-name>/references/if detailed documentation is needed - Update
promptlog/sources.mdwith:- All URLs and documentation sources used
- Purpose of each source
- Key concepts extracted
- Date accessed
- Follow the 7-step skill creation workflow from CLAUDE.md
- Use imperative/infinitive language (not second-person)
- Keep commonly-used context in SKILL.md, detailed references separate
The agent should produce a complete, spec-compliant skill ready for use.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 91 lines · 11 tokens per session scan A 724fa863afbd
research-skill is a command published in the GitHub repository vinnie357/claude-skills (25 stars, last pushed 3d ago), licensed MIT. It adds 11 tokens to every session and 754 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.